ACI Worldwide Launches Innovative ACI Fraud Scoring for Financial Institutions
- Industry-First Fraud-Scoring Platform Uses ACI’s Patented Incremental Learning Technology, Enabling Banks to Reduce Fraud Losses by up to 75 Percent
- New Service Is Available via the Public Cloud to FIs in
North America andEurope
Underpinned by ACI’s award-winning patented Incremental Learning technology, ACI Fraud Scoring Services can enable banks to reduce fraud losses by up to 75 percent. The service is being rolled out in
“We are excited to launch Fraud Scoring Services as part of ACI’s layered approach to machine learning,” said
Key Advantages and Benefits of ACI Fraud Scoring Services:
- Offers complex machine learning capabilities based on ACI’s incremental learning capabilities, improving operational efficiency, increasing fraud detection, and reducing costs
- Patented technology solves machine learning model degradation, retaining efficiency 5X longer than traditional models
- ACI takes on full responsibility for selecting the best model(s) for each customer need, monitoring, and retraining of models as needed, including model governance
- Off-the-shelf model library and shared intelligence available for quick on-boarding and immediate results protecting—from day one—include new payment methods, channels, segments.
- Connected through APIs and offered through ACI’s Public Cloud Environment via Microsoft Azure—minimum latency to be used for real-time decisioning
Incremental learning technology is an integral part of ACI Fraud Management, ACI’s award-winning enterprise fraud management and prevention solution. The solution offers advanced machine learning and behavioral biometrics capabilities, predictive analytics, expertly defined rules, and ACI’s Network Intelligence Technology to help banks identify and mitigate financial crime.
Incremental learning considerably enhances fraud protection for merchants and financial institutions. While traditional machine learning models need to be ‘retrained’ as fraud patterns change, models using incremental learning make small adjustments on an ongoing basis, allowing the model to adapt itself in production when new behaviors are observed.
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katrin.boettger@aciworldwide.com
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